View in EDS HTML Full Text PDF Full Text

Robust Hyperspectral Estimation of Winter Wheat Aboveground Dry Biomass Using CARS-UVE Band Selection and Transfer-Oriented Validation.

Saved in:
Bibliographic Details
Title: Robust Hyperspectral Estimation of Winter Wheat Aboveground Dry Biomass Using CARS-UVE Band Selection and Transfer-Oriented Validation.
Authors: Zhu, Shiyou1 (AUTHOR), Chen, Yulong1,2 (AUTHOR), Wang, Yian1 (AUTHOR), Yang, Sha2 (AUTHOR), Feng, Meichen1 (AUTHOR), Yang, Wude1 (AUTHOR), Bai, Juan1 (AUTHOR), Li, Guangxin1 (AUTHOR) liguangxin@sxau.edu.cn
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1997. 23p.
Subjects: Biomass estimation, Model validation, Support vector machines, Winter wheat, Partial least squares regression, Dry matter content of plants, Spectral reflectance
Abstract: Highlights: A pooled 70/30 split favored SG + CARS-UVE + SVR for winter wheat AGDB estimation. Repeated splits showed overlapping confidence intervals among the primary SG workflows. Full-season and same-window transfer tests changed model ranking and exposed limited transferability. Recurrent blue-green and red-edge regions were more defensible than a single selected-band set. Field hyperspectral sensing can estimate crop biomass, but model ranking may depend strongly on validation design. We evaluated winter wheat aboveground dry biomass (AGDB) estimation using 84 canopy spectra collected across two growing seasons and seven nitrogen-management treatments in Shanxi, China. Six spectral inputs were compared with CARS-UVE band selection, partial least squares regression (PLSR), and support vector regression (SVR). Under a conventional 70/30 pooled split, SG + CARS-UVE + SVR gave the highest apparent accuracy (R2 = 0.8864, RMSE = 0.1174 kg m−2, RPD = 2.9665). This advantage was not stable. Across 20 SG-based repeated splits, CARS-UVE-SVR reached a mean R2 of 0.7413 with a 95% confidence interval of 0.6941–0.7885, similar to full-band PLSR (0.7448, 0.7058–0.7837), and pairwise tests showed no significant R2 advantage. Cross-year transfer further favored simpler latent-variable models: SG + CARS-UVE + PLSR reached R2 = 0.7577 in the 2021 → 2022 direction, whereas the pooled best SVR model dropped to R2 = 0.3402. A stricter same-window cross-year analysis produced weak or negative R2 values, showing that broad phenological biomass gradients supported much of the pooled accuracy. Recurrent selected regions occurred near 436–441 nm, 506–516 nm, and 711–713 nm. These findings suggest that repeated and transfer-oriented validation should be used routinely before hyperspectral biomass models are interpreted for cross-season crop monitoring. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 194915130
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Robust Hyperspectral Estimation of Winter Wheat Aboveground Dry Biomass Using CARS-UVE Band Selection and Transfer-Oriented Validation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Shiyou%22">Zhu, Shiyou</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yulong%22">Chen, Yulong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yian%22">Wang, Yian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Sha%22">Yang, Sha</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+Meichen%22">Feng, Meichen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Wude%22">Yang, Wude</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bai%2C+Juan%22">Bai, Juan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Guangxin%22">Li, Guangxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liguangxin@sxau.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1997. 23p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Biomass+estimation%22">Biomass estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Winter+wheat%22">Winter wheat</searchLink><br /><searchLink fieldCode="DE" term="%22Partial+least+squares+regression%22">Partial least squares regression</searchLink><br /><searchLink fieldCode="DE" term="%22Dry+matter+content+of+plants%22">Dry matter content of plants</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+reflectance%22">Spectral reflectance</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: A pooled 70/30 split favored SG + CARS-UVE + SVR for winter wheat AGDB estimation. Repeated splits showed overlapping confidence intervals among the primary SG workflows. Full-season and same-window transfer tests changed model ranking and exposed limited transferability. Recurrent blue-green and red-edge regions were more defensible than a single selected-band set. Field hyperspectral sensing can estimate crop biomass, but model ranking may depend strongly on validation design. We evaluated winter wheat aboveground dry biomass (AGDB) estimation using 84 canopy spectra collected across two growing seasons and seven nitrogen-management treatments in Shanxi, China. Six spectral inputs were compared with CARS-UVE band selection, partial least squares regression (PLSR), and support vector regression (SVR). Under a conventional 70/30 pooled split, SG + CARS-UVE + SVR gave the highest apparent accuracy (R2 = 0.8864, RMSE = 0.1174 kg m−2, RPD = 2.9665). This advantage was not stable. Across 20 SG-based repeated splits, CARS-UVE-SVR reached a mean R2 of 0.7413 with a 95% confidence interval of 0.6941–0.7885, similar to full-band PLSR (0.7448, 0.7058–0.7837), and pairwise tests showed no significant R2 advantage. Cross-year transfer further favored simpler latent-variable models: SG + CARS-UVE + PLSR reached R2 = 0.7577 in the 2021 → 2022 direction, whereas the pooled best SVR model dropped to R2 = 0.3402. A stricter same-window cross-year analysis produced weak or negative R2 values, showing that broad phenological biomass gradients supported much of the pooled accuracy. Recurrent selected regions occurred near 436–441 nm, 506–516 nm, and 711–713 nm. These findings suggest that repeated and transfer-oriented validation should be used routinely before hyperspectral biomass models are interpreted for cross-season crop monitoring. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194915130
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18121997
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1997
    Subjects:
      – SubjectFull: Biomass estimation
        Type: general
      – SubjectFull: Model validation
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Winter wheat
        Type: general
      – SubjectFull: Partial least squares regression
        Type: general
      – SubjectFull: Dry matter content of plants
        Type: general
      – SubjectFull: Spectral reflectance
        Type: general
    Titles:
      – TitleFull: Robust Hyperspectral Estimation of Winter Wheat Aboveground Dry Biomass Using CARS-UVE Band Selection and Transfer-Oriented Validation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhu, Shiyou
      – PersonEntity:
          Name:
            NameFull: Chen, Yulong
      – PersonEntity:
          Name:
            NameFull: Wang, Yian
      – PersonEntity:
          Name:
            NameFull: Yang, Sha
      – PersonEntity:
          Name:
            NameFull: Feng, Meichen
      – PersonEntity:
          Name:
            NameFull: Yang, Wude
      – PersonEntity:
          Name:
            NameFull: Bai, Juan
      – PersonEntity:
          Name:
            NameFull: Li, Guangxin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 12
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1